5 year CPE Tranmission - Gene Intersection across species by chromosomes and plasmids

Ran the below commands in Unix shell

cd /data02/Analysis/Projects/2_CPE_Transmission/VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis


#---------- Identity plasmids and chromosomes using PlasClass

time for j in $(ls *.fasta); do echo $j; time python3.7 ~/sw/PlasClass/classify_fasta.py -f $j -o "$j".plasclass.probs.out -p 60;  done
real    464m57.242s
user    592m31.525s
sys 133m29.767s

mkdir ../1_plasClass_Probs
mv *.plasclass.probs.out ../1_plasClass_Probs
$ awk '{print FILENAME "\t" $0}' *.out >CPE_Trans_PlasClass_Predictions_comb.tab



#---------- Identify Transposons using Bacant (Integron module is not working properly and Resistance genes are used from AMRfinderplus)
time for d in $(ls *.fasta);  do  
echo "$d";
bacant -n "$d" -o "$d"_bacant_out &
done

mkdir ../2_bacant_annot
mv *_bacant_out ../2_bacant_annot

awk '{$13=$14=""; print FILENAME "\t" $1 "\t" $2}' *_bacant_out/transposon.filter.tsv | fgrep -v 'QUERY' | sed 's/_bacant_out\/transposon.filter.tsv//g' | sed -e 's/_circ[0-9]*/_circ/g' -e 's/\(depth_[0-9]*.[0-9]*x\)[0-9]*/\1/g' | awk '{print $1 "\t" $2 "\t" $1"#"$2 "\t" $3}' >CPE_Trans_BacAnt_Transposons.tab 

awk '{print FILENAME "\t" $0}' *_bacant_out/integron.filter.tsv | fgrep -v 'QUERY' | sed 's/_bacant_out\/integron.filter.tsv//g' | sed 's/[0-9]*\~integron_..//g' | awk '{print $1 "\t" $2 "\t" $1"#"$2 "\t" $3}' >CPE_Trans_BacAnt_Integrons.tab

#---------- Identify IS using Prokka (NOTE: I used an updated ISdb (default ISdb for prokka) located in my desktop)
time awk '{print FILENAME "\t" $0}' *_prokka_out/*.gff | grep 'ISfinder'  | sed 's/ /_/g' | awk '{print $1 "\t" $2 "\t" $NF}' | sed -e 's/ID=.*ISfinder://g' -e 's/;locus_tag.*//g' -e 's/html.2020\/\///g' | sed 's/_prokka_out.*gff/_assembly_renamed.fasta/g' | awk '{print $1 "\t" $2 "\t" $1"#"$2 "\t" $3}' >../../VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis/6_Prokka_InsertionSequences/CPE_Trans_Prokka_IS.tab 



#---------- Identity AMR genes using NCBI AMRfinderplus

# NOTE: Ran this earlier, so just copying the results

mkdir ../3_amrfinderplus_results 

#---------- Identity Virulence factor genes using Abricate

for d in $(ls *.fasta); do echo "abricate -db vfdb $d >"$d".vfdb.tab"; done >abricate_cmds.sh
time parallel --jobs 62 < abricate_cmds.sh &

mkdir ../4_abricate_vf_results 
mv *.vfdb.tab ../4_abricate_vf_results
cd ../4_abricate_vf_results


## Excluding possible contamination samples
-rwxrwxr-x 1 prakki prakki 9.8M Jul  6 14:17 batch12_27072019_ENT1410_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:17 batch2_21032019_ENT466_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:17 batch2_21032019_ENT497_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:18 batch2_21032019_ENT378_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:18 rbatch2_07082020_ENT792_assembly_renamed.fasta

# These assemblies have abnormal assembly sizes. So excluding them from analysis

## -- Reunning MLST 

for d in $(ls *.fasta); do echo "mlst $d >"$d".mlst.tab"; done >mlst_cmds.sh
time parallel --jobs 62 < mlst_cmds.sh &

Ran the below commands in R

library(dplyr)
library(UpSetR)
library(tidyverse)
library(splitstackshape)
library(reshape2)
library(randomcoloR)
library(ComplexHeatmap)
library(data.table)
library(kableExtra)
library(DT)
library(data.table)
library(formattable)




setwd("/data02/Analysis/Projects/2_CPE_Transmission/VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis")


hybridAssemb_1198list <- readLines("/data02/Analysis/Projects/2_CPE_Transmission/VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis/CPE_Hyrbid_Assemblies_1198.list")
#head(hybridAssemb_1198list)

###@@@@@@@@@@@@@@ Dataframe1 - PlasClass Predictions

plasClass_Prob_df <- data.table::fread(file = "1_plasClass_probs/CPE_Trans_PlasClass_Predictions_comb.tab", sep = "\t", header = FALSE)
#head(plasClass_Prob_df)

colnames(plasClass_Prob_df) <- c("Fasta", "Contig", "Probability")

plasClass_Prob_df <- 
  plasClass_Prob_df %>%  
  mutate(Fasta = stringr::str_replace(Fasta, ".plasclass.probs.out", "")) %>% 
  mutate(Fasta_Contig=paste(Fasta, Contig, sep = '#')) %>% 
  mutate(Classification = if_else(Probability >= 0.6, "Plasmid", "Chromosome")) 

#head(plasClass_Prob_df)

###@@@@@@@@@@@@@@ Dataframe2 - MLST

mlst_df <- data.table::fread(file = "5_MLST/mlst_log2.19", sep = "\t", header = FALSE, fill = TRUE)
#head(mlst_df) # mlst_df now has 1289 assembly information
colnames(mlst_df) <- c("Fasta", "Species", "ST", "Gene1", "Gene2", "Gene3", "Gene4", "Gene5", "Gene6", "Gene7")
mlst_df <- mlst_df %>% filter(Fasta %in% hybridAssemb_1198list) # mlst_df now has only 1198 assembly information

Species_Counts <- as.data.frame(table(mlst_df$Species))
colnames(Species_Counts) <- c("Species","SampleCount")

Species_Counts %>% 
  kbl(caption = "Species and Sample Counts") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Species and Sample Counts

Species	SampleCount
	16
cfreundii	75
cronobacter	6
ecloacae	176
ecoli	375
kaerogenes	15
koxytoca	14
kpneumoniae	519
senterica	2

#head(Species_Counts)

#----------------Step1: Combining MLST and plasClass dataframes

plasClass_Prob_df <- left_join(plasClass_Prob_df, mlst_df, by = c("Fasta")) %>% 
    select(Fasta,Contig,Fasta_Contig,Classification,Species, ST) %>% 
    mutate(Species_ContigClass=paste(Species,Classification, sep = '#'))

#head(plasClass_Prob_df)

###@@@@@@@@@@@@@@ Dataframe2 - Bacant Predictions Transposons

bacAnt_df <- data.table::fread(file = "2_bacant_annot/CPE_Trans_BacAnt_Transposons.tab", sep = "\t", header = TRUE)
#head(bacAnt_df)

colnames(bacAnt_df) <- c("Fasta", "Contig", "Fasta_Contig", "Transposon")

#----------------Step2: Combining plasClass dataframe with bacant dataframe for plotting Transposons

bacant_plasClass_df <- left_join(bacAnt_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,Fasta_Contig,Transposon,Classification,Species_ContigClass)

#head(bacant_plasClass_df)

bacant_plasClass_df <- bacant_plasClass_df %>% filter(Fasta.x %in% hybridAssemb_1198list) # bacant_plasClass_df now has only 1198 assembly information

# bacant_plasClass_df %>%
#   select(Transposon,Species_ContigClass) %>%
#   table() %>% # frequency of unique transposons
#   kbl(caption = "Transposons and their counts in Chromosomes and Plasmids across Species") %>%
#   kable_classic(full_width = F, html_font = "Cambria")

customGreen0 = "#DeF7E9"
customRed = "#ff7f7f"

bacant_plasClass_df_total_col = bacant_plasClass_df %>%
  select(Fasta.x,Transposon,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  drop_na(Species_ContigClass) %>% count() %>% c()

Transposons

Table1

Percentages of Samples carrying transposons in Chromsomes and Plasmids across Species

# percentages of transposons (https://clarewest.github.io/blog/post/making-tables-shiny/)
bacant_plasClass_df %>%
  select(Fasta.x,Transposon,Species_ContigClass) %>%
  unique() %>%
  select(Transposon,Species_ContigClass) %>%
  count(Transposon, Species_ContigClass) %>%
  rename(Freq = n) %>%
  drop_na(Species_ContigClass) %>%
  mutate(Species_ContigClass2=Species_ContigClass) %>%
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>%
  left_join(Species_Counts, by = c("Species")) %>%
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>%
  select(Transposon,Species_ContigClass,SamplePrcnt_wGeneElement) %>%
  data.table::dcast(Transposon~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>%
  replace(is.na(.), 0) %>%
  mutate_if(is.numeric, round, digits=1) %>% 
  formattable(., align = c("l",rep("c", bacant_plasClass_df_total_col$n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:bacant_plasClass_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

#head(bacant_plasClass_df)

Table2

Transposons and their unique counts in Chromsomes and Plasmids across Species

bacant_plasClass_df %>% 
  select(Transposon,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  as.data.frame.matrix() %>% 
  kbl(caption = "Transposons and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria") 

Transposons and their unique counts in Chromsomes and Plasmids across Species

	-#Chromosome	-#Plasmid	cfreundii#Chromosome	cfreundii#Plasmid	cronobacter#Plasmid	ecloacae#Chromosome	ecloacae#Plasmid	ecoli#Chromosome	ecoli#Plasmid	kaerogenes#Chromosome	kaerogenes#Plasmid	koxytoca#Chromosome	koxytoca#Plasmid	kpneumoniae#Chromosome	kpneumoniae#Plasmid	senterica#Plasmid
Tn10|AP005147	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
Tn1000|KX709966	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
Tn1721|X61367	0	1	1	1	0	0	0	1	1	0	0	0	0	0	1	0
Tn2|KX242350	0	1	1	0	0	0	1	1	1	0	0	0	0	0	1	0
Tn2003|AY532647	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0
Tn2006|EF127491	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Tn2007|EF059914	1	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0
Tn2008|GQ861438	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Tn2012|EU523120	1	1	1	1	1	1	1	1	1	0	1	0	1	1	1	0
Tn21	1	0	0	1	0	1	1	1	1	0	0	0	0	1	1	0
Tn21-like	1	1	1	1	0	1	1	1	1	0	0	0	0	1	1	0
Tn21|CP001182	1	1	1	1	0	0	1	1	1	0	1	0	0	1	1	0
Tn2555.3|AY485150.1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0
Tn3411|M19532	1	0	1	0	0	0	0	1	1	0	0	0	0	1	0	0
Tn3926|X78059	0	0	1	1	0	1	0	0	1	0	0	0	0	0	1	0
Tn4|HM749966	1	0	1	1	0	0	1	1	1	0	0	0	0	1	1	0
Tn402|KC999035	1	0	0	1	0	1	1	1	1	0	0	0	0	0	1	0
Tn4352|HQ840942	1	0	1	1	0	1	0	1	1	0	0	0	0	1	1	0
Tn4401a|KT378596	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0
Tn4662a	0	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0
Tn501|Z00027	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
Tn5036-like|GQ160960	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	0
Tn5036|Y09025	0	0	1	1	0	0	1	0	1	0	0	0	0	1	1	0
Tn5044/Tn5046	0	0	0	1	0	0	1	0	0	0	0	0	0	1	1	0
Tn5057|AJ302768	1	0	0	0	0	1	1	1	1	0	0	0	0	1	1	0
Tn5060|AJ551280	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
Tn5073|AF461013	0	0	1	1	0	1	1	1	1	0	1	0	1	1	1	0
Tn5074|AF461012	1	1	0	0	0	1	1	1	0	0	0	0	0	0	1	0
Tn5075|AF457211	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
Tn511-3|EU287476	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
Tn512|EU306744	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
Tn5393|M96392	0	0	0	0	0	1	1	1	0	0	0	0	0	0	1	0
Tn5393c|AF313472	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
Tn5403|EU287476	0	1	1	1	1	1	1	1	1	0	1	0	1	1	1	1
Tn5718delta|AM261760	1	0	0	0	0	1	1	1	1	0	0	0	0	1	1	0
Tn6018-L|CP001182	0	0	1	1	0	1	0	1	0	0	0	0	0	0	1	0
Tn602|AH000951	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0
Tn6022|CP001182	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Tn6029|HQ840942	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
Tn6082|AP005147	0	1	0	0	1	1	1	1	1	0	0	0	0	1	1	0
Tn6196|KC999035	0	1	1	1	0	1	1	1	1	0	1	1	0	1	1	0
Tn6205|CP003505	1	1	1	1	1	1	1	1	1	0	1	0	1	1	1	0
Tn6229|KF295829	0	0	0	1	0	1	1	0	0	0	0	0	0	1	1	0
Tn6238|KJ511462	0	0	0	0	0	0	1	1	1	0	0	0	0	1	1	0
Tn6292	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	0
Tn6934|CP046417.1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
Tn7-like|AM261760	1	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
Tn7|AP002527	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
Tn7051|MN915011.1	0	0	0	1	0	0	1	1	1	0	0	0	0	0	1	0
Tn801|AF080442	1	1	1	1	0	0	1	1	1	0	1	0	0	1	1	0
Tn9-like|HQ840942	1	0	1	0	0	1	1	1	1	0	1	0	0	1	1	0
Tn917|FJ208941	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0

Transposons UpsetR

UpsetR Plot Transposons

#----------------Step3: UpsetR Plot Transposons
 
tn_upsetR_df <- bacant_plasClass_df %>% select(Transposon,Species_ContigClass)
#View(tn_upsetR_df)
tn_upsetR_lt <- split(tn_upsetR_df$Transposon, tn_upsetR_df$Species_ContigClass) 
tn_comb_mat = make_comb_mat(tn_upsetR_lt)

#tn_comb_mat
#str(tn_comb_mat)

#as.data.frame(tn_comb_mat)
# comb_mat
# UpSet(tn_comb_mat)
# UpSet(t(comb_mat))
# UpSet(comb_mat, top_annotation = upset_top_annotation(comb_mat, add_numbers = TRUE),
# right_annotation = upset_right_annotation(comb_mat, add_numbers = TRUE))

 
col_size = comb_size(tn_comb_mat)
row_size = set_size(tn_comb_mat)

ht = UpSet(tn_comb_mat, 
      row_title = "Species#SeqClassification", column_title = "Transposon Intersection across species",
      pt_size = unit(6, "pt"),
      lwd = unit(2, "pt"),
      #comb_col = "red",
      # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" , "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
      #bg_col = upset_row_col$Color,
      top_annotation = upset_top_annotation(tn_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                            bar_width = 0.5, annotation_name_rot = 90),
      right_annotation = upset_right_annotation(tn_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
      show_row_names = TRUE,
      row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
      width = unit(700, units = "pt"), height = unit(10, "cm"))

ht = draw(ht)
#ht
col_od = column_order(ht)
row_od = row_order(ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Integrons

Table1

Percentages of Samples carrying Integrons in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe2 - Bacant Predictions Integrons

bacAnt_Integrons_df <- data.table::fread(file = "2_bacant_annot/CPE_Trans_BacAnt_Integrons.tab", sep = "\t", header = TRUE)
#head(bacAnt_Integrons_df)

colnames(bacAnt_Integrons_df) <- c("Fasta", "Contig", "Fasta_Contig", "Integron")

#----------------Step2: Combining plasClass dataframe with bacant dataframe for plotting Transposons

bacAnt_Integrons_plasClass_df <- left_join(bacAnt_Integrons_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,Fasta_Contig,Integron,Classification,Species_ContigClass)

#head(bacAnt_Integrons_plasClass_df)

bacAnt_Integrons_plasClass_df <- bacAnt_Integrons_plasClass_df %>% filter(Fasta.x %in% hybridAssemb_1198list) # bacAnt_Integrons_plasClass_df now has only 1198 assembly information

# bacAnt_Integrons_plasClass_df %>%
#   select(Integron,Species_ContigClass) %>%
#   table() %>% # frequency of unique Integrons
#   kbl(caption = "Integrons and their counts in Chromsomes and Plasmids across Species") %>%
#   kable_classic(full_width = F, html_font = "Cambria")

bacAnt_Integrons_plasClass_df_total_col = bacAnt_Integrons_plasClass_df %>%
  select(Fasta.x,Integron,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  count() %>% c()

# percentages of Integrons
bacAnt_Integrons_plasClass_df %>% 
  select(Fasta.x,Integron,Species_ContigClass) %>%
  unique() %>% 
  select(Integron,Species_ContigClass) %>% 
  count(Integron, Species_ContigClass) %>% 
  rename(Freq = n) %>%  
  drop_na(Species_ContigClass) %>% 
  mutate(Species_ContigClass2=Species_ContigClass) %>% 
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>% 
  left_join(Species_Counts, by = c("Species")) %>% 
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>% 
  select(Integron,Species_ContigClass,SamplePrcnt_wGeneElement) %>% 
  data.table::dcast(Integron~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>% 
  replace(is.na(.), 0) %>% 
  mutate_if(is.numeric, round, digits=1) %>% 
  #kbl(caption = "Sample count percentages for the Integrons in Chromsomes and Plasmids across Species") %>% 
  #kable_classic(full_width = F, html_font = "Cambria")
  formattable(., align = c("l",rep("c", bacAnt_Integrons_plasClass_df_total_col$n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:bacAnt_Integrons_plasClass_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

bacAnt_Integrons_plasClass_df %>% 
  select(Integron,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique Integrons
  kbl(caption = "Integrons and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Integrons and their unique counts in Chromsomes and Plasmids across Species

	-#Chromosome	-#Plasmid	cfreundii#Chromosome	cfreundii#Plasmid	cronobacter#Plasmid	ecloacae#Chromosome	ecloacae#Plasmid	ecoli#Chromosome	ecoli#Plasmid	kaerogenes#Plasmid	koxytoca#Plasmid	kpneumoniae#Chromosome	kpneumoniae#Plasmid
In1000|KF921521	0	0	0	0	0	0	0	0	0	0	0	0	1
In1002|KF921555	0	0	0	0	0	0	0	0	1	0	0	0	0
In1003|KF921556	0	0	0	0	0	0	0	1	1	0	0	0	1
In1004|KF921558	0	1	0	0	0	0	0	1	1	0	0	0	1
In1015|KJ420612	0	0	0	0	0	0	1	0	0	0	0	0	1
In104|AY463797	0	0	0	0	0	0	0	0	1	0	0	0	0
In1069|KM589497	0	0	0	1	0	0	1	1	0	0	0	1	1
In1114|KF914309	0	0	0	0	0	0	0	1	1	0	0	0	0
In1133|KM595287	0	0	0	0	0	1	0	0	0	0	0	0	0
In1207|KP901091	0	0	0	0	0	0	0	0	1	0	0	0	0
In1227|KR699626	0	0	0	0	0	0	0	0	0	0	0	0	1
In1239|KT290951	0	1	0	0	0	1	0	0	1	0	0	0	1
In1249|KT316808	0	0	1	0	0	0	0	1	1	0	0	0	1
In1330|MF612148	0	0	0	1	0	0	0	1	1	0	0	1	1
In1368|KY047413	0	0	1	0	0	0	0	0	0	0	0	1	0
In1372|LC224310	0	0	0	0	0	0	0	0	0	0	0	0	1
In141|FJ711659	0	0	0	1	0	0	0	0	1	0	0	0	1
In1411|KY426918	0	0	0	0	0	0	0	1	1	0	0	0	0
In1440|LC318534	1	0	0	0	0	0	0	1	1	0	0	0	0
In1469|LC333379	0	0	0	1	0	0	1	0	0	0	0	1	1
In149|FJ457611	0	0	0	0	0	1	0	0	0	0	0	0	1
In152|HQ386839	0	0	0	0	0	0	0	0	0	0	0	0	1
In153|JF701188	0	0	0	0	0	0	0	0	0	0	0	0	1
In193|DQ647028	0	0	0	0	0	0	1	0	0	0	0	0	1
In2-10|KF188201	1	0	1	0	0	0	0	1	0	0	0	0	0
In2-3|JX867127	0	0	0	0	0	0	0	0	0	0	0	1	0
In2-30|AB234887	0	0	0	0	0	0	0	0	0	0	0	1	0
In2-44|FJ591055	0	0	1	0	0	0	0	1	0	0	0	0	0
In206|FM957884	0	0	0	0	0	0	1	0	0	0	0	0	1
In221|DQ372710	0	0	0	0	0	0	0	0	1	0	0	0	0
In240|EF368053	0	1	0	1	0	0	1	1	0	1	0	0	1
In244|KC543497	0	0	0	0	0	1	0	0	0	0	0	0	0
In261|FN824509	0	0	0	0	0	0	1	1	0	0	0	0	0
In262|FN823039	0	0	0	0	0	0	0	0	0	0	0	0	1
In282|AY522431	0	0	0	0	0	0	0	1	1	0	0	1	0
In29|AF329699	0	0	0	0	0	0	0	0	0	0	0	0	1
In366|AY069972	0	0	0	0	0	0	1	0	0	0	0	0	0
In384|FM877477	0	0	0	0	0	0	0	0	1	0	0	0	0
In404|DQ520934	0	0	0	0	0	0	0	0	1	0	0	0	1
In415|EU934512	0	0	0	0	0	0	1	0	0	0	0	0	0
In440|AF227505	1	0	0	0	0	0	0	0	1	0	0	0	1
In473|FJ854362	1	0	0	0	0	0	0	1	0	0	0	0	0
In49|EU434618	0	0	0	1	0	0	1	0	0	0	0	1	1
In498|AY214164	1	1	1	1	1	1	1	1	1	1	1	1	1
In504|GU060321	0	0	0	0	0	1	0	1	1	0	0	0	1
In510|EU853659	0	0	0	1	0	1	1	1	1	1	0	1	1
In516|DQ323053	0	0	0	0	0	1	0	0	0	0	0	0	0
In538|KC200566	0	0	0	1	0	0	1	0	1	0	0	0	1
In54|AF220757	0	0	0	0	0	0	0	1	1	0	0	0	0
In545|EU259884	0	0	0	0	0	0	0	0	0	0	0	0	1
In546|FJ594766	0	0	0	0	0	0	0	0	0	0	0	0	1
In554|AJ971343	0	0	0	1	0	0	0	0	0	0	0	0	0
In555|EU675686	0	0	0	1	0	0	1	0	1	0	0	0	1
In556|HQ170516	0	0	0	1	0	1	1	0	1	0	0	0	1
In570|AF453998	0	0	1	0	0	1	1	1	1	0	0	0	0
In573|AM237806	0	0	0	0	0	0	0	1	0	0	0	0	0
In578|FN396877	0	0	0	0	0	0	0	0	0	0	0	0	1
In62|FJ594767	0	0	0	0	0	0	0	0	0	0	0	0	1
In653|HQ730120	0	0	0	0	0	0	0	0	1	0	0	0	0
In663|HQ875011	0	0	0	0	0	0	0	0	1	0	0	0	1
In710|EU434603	0	0	0	0	0	0	0	0	0	0	0	0	1
In718|JF729199	0	0	0	0	0	0	0	1	1	0	0	0	1
In73|AF322577	0	0	0	0	0	0	0	0	1	0	0	0	0
In761|AF175203	0	0	0	0	0	0	0	1	0	0	0	0	1
In771|JX131371	0	0	0	0	0	0	1	0	0	0	0	0	0
In784|JQ837988	0	0	0	0	0	0	0	0	1	0	0	0	0
In79|HQ730118	0	0	0	1	0	0	0	0	0	0	0	0	1
In792|JQ823014	0	0	0	0	0	0	0	0	0	0	0	0	1
In800|JX041889	0	0	0	0	0	0	1	0	0	0	0	0	0
In879|JX885645	0	0	1	0	0	0	0	1	1	0	0	0	1
In901|AY127872	0	0	0	0	0	0	0	0	1	0	0	0	0
In970|KF525327	0	0	0	1	0	0	0	0	0	0	0	0	0

Integrons UpsetR

#----------------Step3: UpsetR Plot Integrons

integron_upsetR_df <- bacAnt_Integrons_plasClass_df %>% select(Integron,Species_ContigClass)
#View(integron_upsetR_df)
integron_upsetR_lt <- split(integron_upsetR_df$Integron, integron_upsetR_df$Species_ContigClass) 
integron_comb_mat = make_comb_mat(integron_upsetR_lt)

#integron_comb_mat
#str(integron_comb_mat)

#as.data.frame(integron_comb_mat)
# comb_mat
#UpSet(integron_comb_mat)


int_col_size = comb_size(integron_comb_mat)
int_row_size = set_size(integron_comb_mat)

integron_ht = UpSet(integron_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "Integron Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
#           bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" , "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(integron_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(integron_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(700, units = "pt"), height = unit(10, "cm"))

ht = draw(integron_ht)
#ht
int_col_od = column_order(integron_ht)
int_row_od = row_order(integron_ht)

decorate_annotation("intersection_size", {
  grid.text(int_col_size[int_col_od], 
            seq_len(length(int_col_size)), 
            unit(int_col_size[int_col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

AMR Genes

Table1

Percentages of Samples carrying AMR genes in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe3 - AMRfinderplus - AMR Genes

amrFinderplus_df <- data.table::fread(file = "3_amrfinderplus_results/CPE_Trans_AMRFinderplus_AMR_Genes.tab", sep = "\t", header = TRUE)
#head(amrFinderplus_df)

colnames(amrFinderplus_df) <- c("Fasta", "Contig", "AMR_GENE", "AMR_GENE_CLASS","Coverage","Identity")

amrFinderplus_df <- amrFinderplus_df %>% filter(Fasta %in% hybridAssemb_1198list) # bacant_plasClass_df now has only 1198 assembly information

amrFinderplus_df <- 
  amrFinderplus_df %>%  
  mutate(Fasta_Contig=paste(Fasta, Contig, sep = '#'))

#----------------Step2: Combining plasClass dataframe with AMRFinderplus dataframe for plotting AMR genes

amrFinderplus_df <- left_join(amrFinderplus_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,AMR_GENE,AMR_GENE_CLASS,Classification,Species_ContigClass)

amrFinderplus_df_total_col = amrFinderplus_df %>%
  select(Fasta.x,AMR_GENE,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  count() %>% c()

# percentages of AMR_GENEs
amrFinderplus_df %>% 
  select(Fasta.x,AMR_GENE,Species_ContigClass) %>%
  unique() %>% 
  select(AMR_GENE,Species_ContigClass) %>% 
  count(AMR_GENE, Species_ContigClass) %>% 
  rename(Freq = n) %>%  
  drop_na(Species_ContigClass) %>% 
  mutate(Species_ContigClass2=Species_ContigClass) %>% 
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>% 
  left_join(Species_Counts, by = c("Species")) %>% 
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>% 
  select(AMR_GENE,Species_ContigClass,SamplePrcnt_wGeneElement) %>% 
  data.table::dcast(AMR_GENE~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>% 
  replace(is.na(.), 0) %>% 
  mutate_if(is.numeric, round, digits=1) %>% 
  #kbl(caption = "Sample count percentages for the AMR genes in Chromsomes and Plasmids across Species") %>% 
  #kable_classic(full_width = F, html_font = "Cambria")
  formattable(., align = c("l",rep("c", amrFinderplus_df_total_col$n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:amrFinderplus_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5,10,25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

### AMR Genes and their unique counts in Chromsomes and Plasmids across Species

amrFinderplus_df %>% 
  select(AMR_GENE,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  kbl(caption = "AMR Genes and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

AMR Genes and their unique counts in Chromsomes and Plasmids across Species

	-#Chromosome	-#Plasmid	cfreundii#Chromosome	cfreundii#Plasmid	cronobacter#Chromosome	cronobacter#Plasmid	ecloacae#Chromosome	ecloacae#Plasmid	ecoli#Chromosome	ecoli#Plasmid	kaerogenes#Chromosome	kaerogenes#Plasmid	koxytoca#Chromosome	koxytoca#Plasmid	kpneumoniae#Chromosome	kpneumoniae#Plasmid	senterica#Plasmid
aac(3)-IId	1	1	1	1	0	1	1	1	1	1	0	0	0	1	0	1	0
aac(3)-IIe	0	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0
aac(3)-IIg	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
aac(3)-IVa	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0	1	0
aac(6’)-Ib	1	1	0	1	0	0	0	1	0	1	0	0	0	0	1	1	0
aac(6’)-Ib-cr5	0	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0
aac(6’)-Ib’	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
aac(6’)-Ib3	0	0	0	1	0	0	1	1	1	1	0	0	0	0	0	1	0
aac(6’)-Ib4	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0
aac(6’)-If	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
aac(6’)-IIc	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
aac(6’)-Il	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
aacA34	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
aadA1	1	1	1	1	0	0	1	1	1	1	0	1	0	0	1	1	0
aadA16	0	1	0	1	0	1	0	1	0	1	0	1	0	0	0	1	0
aadA2	1	1	0	1	0	0	1	1	1	1	0	1	0	0	1	1	0
aadA22	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
aadA5	1	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0
aadA8	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ampC	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ant(2’’)-Ia	0	1	1	1	0	0	0	1	1	0	0	0	0	0	0	1	0
aph(3’’)-Ib	1	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	0
aph(3’)-Ia	1	0	0	1	0	0	1	0	1	1	0	0	0	0	1	1	0
aph(3’)-IIa	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
aph(3’)-VI	0	0	0	1	0	0	1	1	1	1	0	0	0	0	0	1	0
aph(3’)-XV	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0
aph(4)-Ia	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0	1	0
aph(6)-Id	1	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	0
armA	0	1	0	1	0	1	0	1	1	1	0	1	0	1	1	1	0
arr	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
arr-2	0	0	0	1	0	0	0	1	0	1	0	1	0	0	1	1	0
arr-3	0	1	0	1	0	1	0	1	0	1	0	1	0	0	1	1	0
blaACT-1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-16	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-17	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-24	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-25	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-28	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-3	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-40	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-45	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
blaACT-49	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-51	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-52	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-53	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-64	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-65	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-69	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-74	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-90	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaCARB-2	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0
blaCMH-4	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaCMY-109	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-148	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaCMY-152	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-159	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-2	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
blaCMY-35	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-39	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-4	0	0	0	0	0	1	0	1	0	1	0	0	0	0	0	1	0
blaCMY-42	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaCMY-48	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-6	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
blaCMY-65	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-66	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-75	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-84	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCTX-M-14	1	0	1	0	0	0	1	0	1	1	0	0	0	0	0	1	0
blaCTX-M-15	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0
blaCTX-M-27	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaCTX-M-3	1	1	0	1	0	0	0	1	0	1	0	1	0	0	0	1	0
blaCTX-M-55	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0
blaCTX-M-65	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaCTX-M-8	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaCTX-M-9	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
blaDHA-1	1	0	0	1	0	0	1	0	0	1	0	1	0	0	1	1	0
blaDHA-15	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaDHA-17	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaDHA-21	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaDHA-4	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaEC-5	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
blaGES-9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaIMI-1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaIMP-1	0	1	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0
blaIMP-26	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaIMP-4	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0
blaKPC-2	0	1	0	1	0	1	0	1	1	1	0	1	0	1	1	1	0
blaKPC-6	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
blaLAP-1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
blaLAP-2	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	1	0
blaLEN-17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaMAL-1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
blaMAL-2	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
blaMIR-20	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaMIR-5	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaNDM-1	0	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	1
blaNDM-4	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaNDM-5	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	1	0
blaNDM-7	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	1	0
blaNDM-9	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaOKP-A-11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-A-17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-A-9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-34	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOXA-1	1	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0
blaOXA-10	0	0	0	1	0	0	0	1	0	1	0	1	0	0	0	1	0
blaOXA-181	0	0	1	0	0	0	0	1	0	1	0	0	0	0	1	1	0
blaOXA-2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
blaOXA-21	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaOXA-23	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaOXA-232	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
blaOXA-48	0	0	0	1	0	1	0	1	0	1	0	0	0	0	0	1	0
blaOXA-9	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	1	0
blaOXY-1-1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
blaOXY-1-2	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
blaOXY-2-1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
blaPER-7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaSFO-1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0
blaSHV-1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0
blaSHV-108	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-11	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0
blaSHV-12	1	0	0	1	0	0	1	1	1	1	0	1	0	0	1	1	0
blaSHV-144	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-186	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-187	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-26	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-27	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-28	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-2A	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaSHV-32	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-33	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-41	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-5	0	0	0	1	0	0	0	1	0	1	0	0	0	0	1	1	0
blaSHV-52	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-60	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-62	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-71	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-75	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-76	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-77	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaTEM-1	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0
blaTEM-116	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0
blaTEM-135	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaTEM-169	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaTEM-176	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
blaTEM-190	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaTEM-2	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaTEM-215	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaTEM-30	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaTEM-31	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaTEM-32	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaVIM-4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
ble	0	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	1
catA1	1	0	1	0	0	0	1	1	1	1	0	1	0	0	0	1	0
catA2	0	0	1	1	0	0	1	1	0	1	0	0	0	0	0	1	0
catB	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	1	0
catB3	0	1	0	1	0	0	0	1	1	1	0	0	0	0	1	1	0
catB8	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
cmlA1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
cmlA5	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	1	0
dfrA1	1	0	1	1	0	0	1	1	1	0	0	0	0	0	1	1	0
dfrA12	1	0	0	1	0	0	1	1	1	1	0	1	0	0	1	1	0
dfrA14	0	1	0	1	0	0	1	1	1	1	0	1	0	1	1	1	0
dfrA15	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0
dfrA16	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0
dfrA17	1	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0
dfrA23	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
dfrA27	0	1	0	1	0	1	0	1	0	1	0	1	0	0	0	1	0
dfrA29	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
dfrA32	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0
dfrA33	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0
dfrA5	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
dfrA7	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
dfrB4	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ere(A)	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0
erm(42)	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
floR	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	1	0
fosA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
fosA10	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0
fosA3	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
fosA4	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
fosA7.5	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
lnu(F)	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
mcr-1.1	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0
mcr-10.1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	1	0
mcr-3.1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
mcr-4.3	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
mef(B)	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
mph(A)	1	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	0
mph(E)	0	1	0	1	0	1	0	1	1	1	0	1	0	1	1	1	0
msr(E)	0	1	0	1	0	1	0	1	1	1	0	1	0	1	1	1	0
oqxA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxA10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxA11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB14	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB19	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB20	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB25	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB32	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
qacEdelta1	1	1	1	1	0	1	1	1	1	1	0	1	0	0	1	1	0
qacG2	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0
qacL	0	1	1	1	0	0	0	1	1	1	0	0	0	0	0	1	0
qepA1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
qepA4	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
qepA8	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
qnrA1	0	0	0	1	0	0	0	1	1	1	0	0	0	0	0	1	0
qnrB1	0	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0
qnrB12	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB17	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB2	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0
qnrB28	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB35	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB38	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB4	0	0	0	1	0	0	1	0	0	1	0	1	0	0	1	1	0
qnrB6	0	1	1	1	0	0	0	0	0	1	0	1	0	0	0	1	0
qnrB69	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB7	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0
qnrB9	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0
qnrS1	0	1	1	1	0	0	0	1	1	1	0	1	0	0	0	1	0
qnrS2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
rmtB1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
rmtC	0	1	0	0	0	0	1	1	0	1	0	0	0	0	0	1	0
rmtF1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	1	0
rmtF2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
sat2	1	0	1	1	0	0	1	0	1	0	0	0	0	0	1	0	0
smr	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
sul1	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0
sul2	1	1	0	1	0	0	1	1	1	1	0	1	0	1	1	1	0
sul3	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
tet(A)	1	1	0	1	0	0	1	1	1	1	0	1	0	1	1	1	0
tet(B)	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
tet(C)	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	1	0
tet(D)	0	0	0	1	0	0	0	1	0	1	0	1	0	0	1	1	0
tet(G)	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
tet(M)	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
tet(X4)	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0

AMR Genes UpsetR

#----------------Step3: UpsetR Plot AMR Genes
amr_upsetR_df <- amrFinderplus_df %>% select(AMR_GENE,Species_ContigClass)

amr_upsetR_lt <- split(amr_upsetR_df$AMR_GENE, amr_upsetR_df$Species_ContigClass) 
amr_comb_mat = make_comb_mat(amr_upsetR_lt)

col_size = comb_size(amr_comb_mat)
row_size = set_size(amr_comb_mat)

amr_ht = UpSet(amr_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "AMR Genes Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
           # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" ,
           #            "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(amr_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(amr_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(900, units = "pt"), height = unit(12, "cm"))

amr_ht = draw(amr_ht)
#ht
col_od = column_order(amr_ht)
row_od = row_order(amr_ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

VF Genes

Table1

Percentages of Samples carrying Virulence genes in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe4 - Abricate - Virulence Factor Genes

Abricate_vf_df <- data.table::fread(file = "4_abricate_vf_results/CPE_Trans_Abricate_VF_Genes.tab", sep = "\t", header = TRUE)
#head(Abricate_vf_df)

##########-------------- Modifying dataframe START -------------------################

Abricate_vf_df <- Abricate_vf_df %>% select(`#FILE`,SEQUENCE,START,END,STRAND,GENE)  
colnames(Abricate_vf_df ) <- c("Fasta", "Contig","START","END","STRAND","VF_GENE")

Abricate_vf_df <- 
  Abricate_vf_df %>%  
  mutate(Fasta_Contig=paste(Fasta, Contig, sep = '#'))

#head(Abricate_vf_df)

#----------------Step2: Combining plasClass dataframe with abricate_vf_dataframe for plotting virulence genes

Abricate_vf_df <- left_join(Abricate_vf_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,VF_GENE,Classification,Species_ContigClass)

Abricate_vf_df_total_col = Abricate_vf_df %>%
  select(Fasta.x,VF_GENE,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  drop_na(Species_ContigClass) %>% count() %>% c()

# percentages of AMR_GENEs
Abricate_vf_df %>% 
  select(Fasta.x,VF_GENE,Species_ContigClass) %>%
  unique() %>% 
  select(VF_GENE,Species_ContigClass) %>% 
  count(VF_GENE, Species_ContigClass) %>% 
  rename(Freq = n) %>%  
  drop_na(Species_ContigClass) %>% 
  mutate(Species_ContigClass2=Species_ContigClass) %>% 
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>% 
  left_join(Species_Counts, by = c("Species")) %>% 
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>% 
  select(VF_GENE,Species_ContigClass,SamplePrcnt_wGeneElement) %>% 
  data.table::dcast(VF_GENE~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>% 
  replace(is.na(.), 0) %>% 
  mutate_if(is.numeric, round, digits=1) %>% 
 # kbl(caption = "Sample count percentages for the Virulence factor genes in Chromsomes and Plasmids across Species") %>% 
 # kable_classic(full_width = F, html_font = "Cambria")
  formattable(., align = c("l",rep("c", Abricate_vf_df_total_col $n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:Abricate_vf_df_total_col $n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

Virulence genes and their unique counts in Chromsomes and Plasmids across Species

Abricate_vf_df %>% 
  select(VF_GENE,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  kbl(caption = "Virulence factors genes and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Virulence factors genes and their unique counts in Chromsomes and Plasmids across Species

	-#Chromosome	cfreundii#Chromosome	cfreundii#Plasmid	cronobacter#Chromosome	ecloacae#Chromosome	ecloacae#Plasmid	ecoli#Chromosome	ecoli#Plasmid	kaerogenes#Chromosome	koxytoca#Chromosome	kpneumoniae#Chromosome	kpneumoniae#Plasmid	NA#Chromosome	NA#Plasmid	senterica#Chromosome
aap/aspU	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
afaA	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
afaB-I	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
afaC-I	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
afaD	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
afaE-I	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
aslA	1	1	0	0	0	0	1	0	0	0	1	0	1	0	0
astA	0	1	0	0	0	1	1	1	0	1	1	1	1	1	0
cesAB	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
cesD	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
cesD2	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
cesL	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
cesT	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
cheY	1	1	0	0	0	0	0	0	0	0	0	0	1	0	0
chuA	0	0	0	1	0	0	1	0	0	0	0	0	1	0	1
chuS	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
chuT	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
chuU	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
chuV	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
chuW	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
chuX	0	1	0	1	0	0	1	0	0	0	0	0	1	0	1
chuY	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
cnf1	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0
csgA	1	1	0	0	0	0	0	0	0	0	0	0	1	0	0
csgB	1	1	0	1	0	0	1	0	0	0	0	0	1	0	1
csgD	1	1	0	1	1	0	1	0	0	0	0	0	1	0	1
csgE	1	1	0	1	0	0	0	0	0	0	0	0	1	0	1
csgF	1	1	0	1	0	0	1	0	0	0	0	0	1	0	1
csgG	1	1	0	1	1	0	1	0	0	0	0	0	1	0	1
csnA	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
daaD	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
daaE	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
daaF	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
draA	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
draD	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
draE2	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
draP	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
eae	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
east1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
entA	0	1	0	1	1	0	1	0	1	1	1	0	1	0	1
entB	1	1	0	1	1	0	1	0	1	1	1	0	1	0	1
entC	1	1	0	0	0	0	1	0	0	0	0	0	1	0	0
entD	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
entE	1	1	0	1	0	0	1	0	0	0	0	0	1	0	1
entF	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
entS	1	1	0	1	0	0	1	0	0	0	0	0	1	0	1
escC	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escD	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escE	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escF	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escG	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escI	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escJ	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escL	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escN	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escO	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escP	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escR	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escS	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escT	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escU	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
escV	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
espA	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
espB	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
espD	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
espG	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
espK	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
espL1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
espL4	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
espR1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
espR3	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
espR4	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
espX1	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0
espX2	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
espX4	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
espX5	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
espY1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
espY2	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
espY3	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
espY4	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
etgA	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
faeC	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
faeD	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
faeE	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
faeF	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
faeH	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
faeI	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
faeJ	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
fdeC	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
fepA	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
fepB	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
fepC	1	1	0	1	0	0	1	0	1	0	1	0	1	0	1
fepD	1	1	0	0	1	0	1	0	0	0	1	0	1	0	0
fepG	1	1	0	1	0	0	1	0	0	0	1	0	1	0	0
fes	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
fimA	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
fimB	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
fimC	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
fimD	0	1	0	1	0	0	1	0	0	0	0	0	1	0	1
fimE	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0
fimF	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
fimG	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
fimH	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
fimI	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
flgH	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0
fliG	1	1	0	1	0	0	1	0	1	0	0	0	1	0	1
fliM	1	0	0	0	0	0	1	0	1	0	0	0	1	0	0
fliN	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0
fliP	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
focA	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
focC	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
focD	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
focF	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
focG	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
focH	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
fyuA	0	1	0	1	1	0	1	0	1	1	1	1	1	0	1
GENE	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
gspC	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
gspD	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
gspE	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
gspF	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
gspG	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
gspH	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
gspI	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
gspJ	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
gspK	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
gspL	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
gspM	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
gtrA	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0
gtrB	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0
hlyA	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0
hlyB	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0
hlyC	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0
hlyD	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0
ibeA	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
iroB	0	0	0	0	1	0	1	1	1	0	0	1	1	0	0
iroC	0	0	0	0	1	0	1	1	0	0	0	1	1	0	0
iroD	0	0	0	0	1	0	1	1	0	0	0	1	1	0	0
iroE	0	0	0	0	1	0	1	1	0	0	0	1	1	0	0
iroN	0	0	0	0	1	0	1	1	1	0	0	1	1	0	0
irp1	0	1	0	1	1	0	1	0	1	1	1	1	1	0	1
irp2	0	1	0	1	1	0	1	0	1	1	1	1	1	0	1
iucA	0	0	1	1	0	0	1	1	0	0	0	1	1	1	1
iucB	0	0	1	1	0	0	1	1	0	0	0	1	1	1	1
iucC	0	0	1	1	0	0	1	1	0	0	0	1	1	1	1
iucD	0	0	1	0	0	0	1	1	0	0	0	1	1	1	0
iutA	0	0	1	1	0	0	1	1	0	0	0	1	1	1	1
kpsD	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
kpsM	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
kpsT	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
lpfA	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
lpfB	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
lpfC	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
lpfE	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
map	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
mgtB	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0
nleB2	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ompA	1	1	0	1	1	0	1	0	1	1	1	0	1	0	1
paa	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
papA	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
papB	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0
papC	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0
papD	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0
papE	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
papF	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0
papG	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
papH	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0
papI	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0
papJ	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0
papK	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0
papX	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0
pic	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
sat	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0
senB	0	0	0	1	0	0	0	1	0	0	0	0	0	1	1
sepD	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sepL	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sepQ/escQ	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sfaA	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sfaB	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sfaC	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sfaD	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sfaE	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sfaF	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sfaG	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sfaH	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sfaS	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sfaX	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sfaY	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
shuA	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
shuS	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
shuT	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
shuX	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
shuY	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
sinH	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
tcpC	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
tviB	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0
tviC	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0
tviD	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0
tviE	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0
vat	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
vexA	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0
vexB	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0
vexC	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0
vexD	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0
vexE	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0
yagV/ecpE	0	0	1	0	0	1	1	0	0	0	1	0	1	0	0
yagW/ecpD	0	0	1	0	0	1	1	0	1	0	1	0	1	0	0
yagX/ecpC	0	0	1	0	0	1	1	0	0	0	1	0	1	0	0
yagY/ecpB	0	0	1	0	0	0	1	0	0	0	1	0	1	0	0
yagZ/ecpA	0	0	1	0	0	1	1	0	1	1	1	0	1	0	0
ybtA	0	1	0	1	1	0	1	0	1	1	1	1	1	0	1
ybtE	0	1	0	1	1	0	1	0	1	1	1	1	1	0	1
ybtP	0	1	0	1	1	0	1	0	1	1	1	1	1	0	1
ybtQ	0	1	0	1	1	0	1	0	1	1	1	1	1	0	1
ybtS	1	1	0	1	1	0	1	0	1	1	1	1	1	0	1
ybtT	0	1	0	1	1	0	1	0	1	1	1	1	1	0	1
ybtU	0	1	0	1	1	0	1	0	1	1	1	1	1	0	1
ybtX	0	1	0	1	1	0	1	0	1	1	1	1	1	0	1
ykgK/ecpR	0	0	1	0	0	1	1	0	0	0	1	0	1	0	0

VF Genes UpsetR

#----------------Step3: UpsetR Plot VFGenes

abr_upsetR_df <- Abricate_vf_df %>% select(VF_GENE,Species_ContigClass)
#View(abr_upsetR_df)

abr_upsetR_lt <- split(abr_upsetR_df$VF_GENE, abr_upsetR_df$Species_ContigClass) 
abr_comb_mat = make_comb_mat(abr_upsetR_lt)

col_size = comb_size(abr_comb_mat)
row_size = set_size(abr_comb_mat)

vf_ht = UpSet(abr_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "Virulence factor genes Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
           # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" ,
           #            "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(abr_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(abr_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(900, units = "pt"), height = unit(12, "cm"))

vf_ht = draw(vf_ht)
#ht
col_od = column_order(vf_ht)
row_od = row_order(vf_ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Insertion Sequence (IS)

Table1

Percentages of Samples carrying Insertion Sequences in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe6 - Prokka Insertion Sequences 

Prokka_IS_df <- data.table::fread(file = "6_Prokka_InsertionSequences/CPE_Trans_Prokka_IS.tab", sep = "\t", header = TRUE)
#head(Prokka_IS_df)

colnames(Prokka_IS_df) <- c("Fasta", "Contig", "Fasta_Contig", "IS")

#----------------Step2: Combining plasClass dataframe with Prokka_IS dataframe for plotting Transposons

Prokka_IS_plasClass_df <- left_join(Prokka_IS_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,Fasta_Contig,IS,Classification,Species_ContigClass)

#head(Prokka_IS_plasClass_df)

Prokka_IS_plasClass_df <- Prokka_IS_plasClass_df %>% filter(Fasta.x %in% hybridAssemb_1198list) # Prokka_IS_plasClass_df now has only 1198 assembly information

# Prokka_IS_plasClass_df %>%
#   select(Transposon,Species_ContigClass) %>%
#   table() %>% # frequency of unique transposons
#   kbl(caption = "Transposons and their counts in Chromosomes and Plasmids across Species") %>%
#   kable_classic(full_width = F, html_font = "Cambria")

customGreen0 = "#DeF7E9"
customRed = "#ff7f7f"
Prokka_IS_plasClass_df_total_col = Prokka_IS_plasClass_df %>% 
  select(Fasta.x,IS,Species_ContigClass) %>% 
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  count() %>% c()


# percentages of IS (https://clarewest.github.io/blog/post/making-tables-shiny/)
Prokka_IS_plasClass_df %>%
  select(Fasta.x,IS,Species_ContigClass) %>%
  unique() %>%
  select(IS,Species_ContigClass) %>%
  count(IS, Species_ContigClass) %>%
  rename(Freq = n) %>%
  drop_na(Species_ContigClass) %>%
  mutate(Species_ContigClass2=Species_ContigClass) %>%
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>%
  left_join(Species_Counts, by = c("Species")) %>%
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>%
  select(IS,Species_ContigClass,SamplePrcnt_wGeneElement) %>%
  data.table::dcast(IS~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>%
  replace(is.na(.), 0) %>%
  mutate_if(is.numeric, round, digits=1) %>% 
  formattable(., align = c("l",rep("c", Prokka_IS_plasClass_df_total_col$n)), 
              list(
                `IS` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
                area(col = 1:Prokka_IS_plasClass_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

Insertion Sequences and their unique counts in Chromsomes and Plasmids across Species

Prokka_IS_plasClass_df %>% 
  select(IS,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  kbl(caption = "Insertion Sequence and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Insertion Sequence and their unique counts in Chromsomes and Plasmids across Species

	-#Chromosome	-#Plasmid	cfreundii#Chromosome	cfreundii#Plasmid	cronobacter#Chromosome	cronobacter#Plasmid	ecloacae#Chromosome	ecloacae#Plasmid	ecoli#Chromosome	ecoli#Plasmid	kaerogenes#Chromosome	kaerogenes#Plasmid	koxytoca#Chromosome	koxytoca#Plasmid	kpneumoniae#Chromosome	kpneumoniae#Plasmid	senterica#Chromosome	senterica#Plasmid
IS100	0	1	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0
IS1006	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
IS100kyp	0	1	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0
IS10A	1	0	0	1	0	1	0	1	0	1	0	0	0	0	0	1	0	0
IS10R	1	1	0	1	0	0	1	1	1	1	1	1	0	0	1	1	0	0
IS1133	0	0	0	1	0	0	1	1	1	1	0	0	0	0	0	1	0	0
IS1203	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
IS1222	0	0	0	1	0	0	1	1	0	0	0	1	0	0	0	0	0	0
IS1230B	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0
IS1247	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
IS1271	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
IS1294	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
IS1326	0	0	1	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0
IS1351	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
IS1353	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
IS1394	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
IS1396	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0
IS1397	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0
IS1400	1	0	1	0	1	0	1	0	0	0	1	1	0	0	1	1	0	0
IS1414	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0	0
IS1489v1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
IS1491	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
IS15	0	0	0	1	0	0	0	1	1	1	0	1	0	1	1	1	0	0
IS150	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS1541B	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
IS15DI	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
IS15DII	0	0	1	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
IS15DIV	1	1	1	1	0	1	1	1	1	1	1	1	0	1	1	1	0	0
IS1618	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
IS186A	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS186B	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS1A	0	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0	0
IS1B	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
IS1D	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0
IS1F	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS1G	0	0	1	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
IS1H	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS1N	0	0	1	0	0	0	1	1	1	1	1	0	0	1	1	1	0	0
IS1S	0	0	0	0	0	0	1	1	1	1	0	0	0	0	1	1	0	0
IS1SD	0	0	1	1	0	0	1	0	1	1	0	1	0	1	1	1	0	0
IS1X1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	1	0	0
IS1X2	1	1	1	1	0	1	1	1	1	1	0	1	1	1	1	1	0	0
IS1X3	1	1	1	1	0	1	1	1	1	1	0	0	1	1	1	1	0	0
IS1X4	1	1	1	1	0	0	1	1	1	1	0	1	1	0	1	1	0	0
IS2	0	0	1	1	0	0	1	1	1	1	0	0	1	0	1	1	0	0
IS200C	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS200F	0	0	1	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0
IS21	0	0	1	1	0	0	1	0	1	1	0	0	0	0	0	0	0	0
IS231L	1	0	1	0	0	0	1	0	1	0	0	0	1	0	1	0	0	0
IS285	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0
IS3	0	0	1	1	0	0	1	1	1	1	0	0	0	0	0	1	0	0
IS3000	0	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
IS30D	0	0	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
IS30H	1	0	1	1	0	0	1	1	0	0	0	0	0	0	0	0	0	0
IS3411	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS3F	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
IS3H	0	0	1	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
IS4	0	0	1	1	0	0	1	0	1	1	0	0	1	0	0	0	0	0
IS406	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
IS421	0	0	0	1	0	0	0	1	1	1	0	0	0	0	1	1	0	0
IS4321	0	1	1	1	0	0	1	1	0	1	0	0	0	0	1	1	0	0
IS4321R	1	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	0	0
IS4811	1	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0
IS5075	0	1	1	1	0	0	1	1	1	1	0	1	1	1	1	1	0	0
IS50R	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
IS5564	1	0	1	0	0	0	1	0	1	0	1	0	1	0	1	1	0	0
IS5708	1	1	1	1	0	0	1	1	1	1	0	0	0	1	0	0	0	0
IS5B	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	1	0	0
IS5D	1	1	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
IS606	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
IS609	1	0	1	1	0	0	1	0	1	1	0	0	0	0	1	1	0	0
IS6100	1	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
IS621	0	0	0	0	0	0	1	1	1	1	0	0	0	0	1	0	0	0
IS629	1	0	0	1	0	0	0	1	1	1	0	0	0	0	1	1	0	0
IS630	0	0	1	0	0	0	1	0	1	1	1	1	0	0	1	0	0	0
IS679	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
IS682	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	1	0	0
IS884	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
IS903	0	1	1	1	0	1	1	1	1	1	0	1	1	1	1	1	0	0
IS903B	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	0	0
IS91	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
IS911	0	0	1	1	0	0	1	1	1	1	0	0	0	0	0	0	0	0
ISAav1	0	0	0	0	0	0	1	1	0	1	0	0	0	0	1	1	0	0
ISAba1	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISAba125	0	1	0	1	0	1	1	1	1	1	0	1	0	1	0	1	0	1
ISAba14	0	0	0	1	0	0	1	1	1	1	0	0	0	0	0	1	0	0
ISAba33	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISAba42	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISAba43	1	1	1	0	0	0	0	1	1	1	0	1	0	0	1	1	0	0
ISAba8	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISAbe14	1	0	1	0	0	0	1	1	1	1	0	0	0	0	1	1	0	0
ISAcba1	0	0	0	1	0	0	1	0	1	1	0	0	1	0	1	1	0	0
ISAeca1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISAeme15	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0
ISAeme19	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISAeme9	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISAfe13	0	1	1	1	0	1	1	1	0	1	0	1	0	1	1	1	0	1
ISAlde2	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
ISAlw16	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISApl1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISApu1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISApu2	0	1	1	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISArsp9	1	1	1	1	0	0	1	1	1	1	0	0	0	1	1	1	0	0
ISAs17	0	0	1	1	0	0	0	1	0	1	1	1	0	0	1	1	0	0
ISAs19	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ISAs25	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISAs29	0	0	1	1	0	0	1	1	0	0	0	0	0	0	1	1	0	0
ISAs9	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISAzs36	0	0	1	1	0	0	1	1	0	1	0	1	0	0	0	1	0	0
ISBcen15	0	0	1	0	0	0	1	0	1	0	0	1	0	0	1	1	0	0
ISBdi12	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISBli29	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
ISBli6	1	0	1	1	0	0	0	0	1	1	0	0	0	0	1	1	0	0
ISBmo1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
ISBmu20	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISBos1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISBrsa1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISBsp7	1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
ISBth5	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISCARN93	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISCco2	0	0	0	1	0	0	1	1	1	1	0	0	0	0	0	1	0	0
ISCco3	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ISCep1	1	0	1	0	1	0	1	0	1	1	1	0	1	0	1	0	1	0
ISCfr1	1	1	1	1	0	1	1	1	1	1	0	0	0	1	1	1	0	0
ISCfr12	0	0	0	0	0	0	0	0	1	1	0	1	0	0	1	1	0	0
ISCfr13	0	0	1	0	0	0	1	1	0	0	1	0	0	0	1	1	0	0
ISCfr14	0	0	1	1	0	0	1	1	0	1	0	1	0	0	1	1	0	0
ISCfr26	0	0	1	1	0	0	1	1	0	1	0	0	0	0	0	1	0	0
ISCfr3	0	0	1	1	0	1	0	1	1	1	0	0	0	0	0	1	0	0
ISCfr4	1	0	1	1	0	0	0	0	1	0	1	1	0	0	1	1	0	0
ISCfr6	0	1	0	0	0	0	1	1	1	1	1	0	0	0	1	1	0	0
ISCfr8	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0
ISCgl1	1	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0
ISCot1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	0	0
ISCro1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	1	1	0	0
ISCro2	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISCro3	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISCro4	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0
ISCro5	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ISCro6	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISCte8	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
ISDsh3	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0
ISEae1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
ISEal1	1	0	1	1	0	0	1	1	0	0	0	1	1	1	1	1	0	0
ISEam1	1	0	1	1	0	0	1	1	1	0	1	0	0	0	1	1	0	0
ISEas1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISEc1	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc10	0	0	1	0	0	0	0	0	1	1	0	0	1	1	1	1	0	0
ISEc11	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISEc12	1	0	1	1	0	0	1	0	1	1	0	0	0	0	1	1	0	0
ISEc13	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISEc14	1	0	1	1	0	0	1	1	1	1	0	1	0	0	1	1	0	0
ISEc15	0	1	1	1	0	0	1	1	0	1	0	0	1	0	1	1	0	0
ISEc16	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc17	1	0	1	1	0	0	1	1	1	1	0	0	0	1	1	1	0	0
ISEc18	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc19	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEc20	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEc21	0	0	1	1	0	0	1	0	1	1	0	0	0	1	1	1	0	0
ISEc22	0	0	1	0	0	0	0	0	1	1	0	0	0	0	1	1	0	0
ISEc23	1	1	0	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
ISEc24	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	0	0
ISEc25	1	0	1	1	0	0	1	1	0	1	0	0	0	0	1	1	0	0
ISEc26	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc27	0	0	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
ISEc29	0	1	0	1	0	1	0	1	1	1	0	1	0	1	1	1	0	0
ISEc30	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEc31	0	0	1	0	0	0	1	0	1	1	1	0	0	0	1	1	0	0
ISEc32	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
ISEc33	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	1
ISEc35	0	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
ISEc36	0	0	1	1	0	0	1	1	1	1	0	1	1	0	1	1	0	0
ISEc37	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
ISEc38	1	0	1	1	0	0	0	1	1	1	0	1	0	1	1	1	0	0
ISEc39	1	0	1	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0
ISEc40	0	1	1	1	0	0	0	0	1	0	0	0	1	1	1	1	0	0
ISEc41	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0
ISEc42	0	0	0	1	0	0	0	0	1	1	0	0	1	0	0	1	0	0
ISEc43	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc44	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc45	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc46	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISEc47	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISEc48	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEc49	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEc5	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEc52	1	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
ISEc53	1	0	1	0	0	0	1	0	1	1	1	0	0	0	1	0	0	0
ISEc59	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0
ISEc60	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0
ISEc62	0	0	1	1	0	0	1	1	1	1	0	1	0	0	0	1	0	0
ISEc63	0	1	1	1	0	0	1	1	1	1	0	0	0	1	0	1	0	0
ISEc66	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc68	0	1	1	0	0	0	1	1	1	1	0	0	0	0	1	1	0	0
ISEc76	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc77	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
ISEc78	1	0	1	1	0	0	1	1	1	1	0	0	0	0	0	1	0	0
ISEc8	0	0	1	1	0	0	1	1	1	1	0	1	0	0	1	1	0	0
ISEc81	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0
ISEc83	1	0	1	0	0	0	1	0	1	1	0	0	0	0	1	0	0	0
ISEc84	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISEc86	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc9	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
ISEcB1	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ISEch11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISEch12	1	0	1	0	0	0	0	0	1	0	1	1	0	0	1	1	0	0
ISEch14	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEch2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEch6	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEcl1	0	1	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
ISEcl10	1	0	1	0	1	0	1	0	1	1	0	0	0	0	1	0	0	0
ISEcl11	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISEcl3	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEcl6	0	0	1	0	1	0	1	1	1	0	0	1	1	1	1	1	0	0
ISEhe2	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0
ISEhe3	1	0	1	1	1	0	1	1	1	1	1	1	1	1	1	1	0	0
ISEhe4	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	1	0	0
ISEic2	0	0	1	0	1	0	0	1	1	1	0	0	0	0	1	1	0	0
ISEisp1	1	0	1	0	0	0	1	0	1	0	1	0	0	0	1	0	0	0
ISErsp1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0
ISEsa1	0	1	1	1	0	0	1	1	1	0	0	0	0	1	1	1	0	0
ISEsa2	0	1	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
ISEsp1	0	0	1	1	1	0	1	1	1	1	0	0	1	1	1	1	0	0
ISGme8	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISHce1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISHgi14	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISHne2	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0
ISHp1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISIde1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISKox1	0	0	0	1	0	0	1	1	1	0	1	0	0	0	1	1	0	0
ISKox2	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISKox3	1	1	1	1	0	0	1	1	1	1	1	0	0	0	1	1	0	0
ISKpn1	1	0	1	1	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISKpn11	0	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0	0
ISKpn12	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0
ISKpn14	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
ISKpn15	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISKpn18	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0
ISKpn19	0	1	1	1	0	0	0	1	1	1	0	1	0	0	1	1	0	0
ISKpn2	0	0	1	0	0	0	1	0	1	0	0	0	1	0	1	1	0	0
ISKpn20	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0
ISKpn21	1	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	0	0
ISKpn24	1	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
ISKpn25	1	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
ISKpn26	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	0	0
ISKpn27	0	1	1	1	0	1	0	1	1	1	0	1	0	1	1	1	0	0
ISKpn28	0	0	0	1	0	0	0	1	1	1	0	1	1	1	1	1	0	0
ISKpn3	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISKpn31	1	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0	0
ISKpn33	0	0	1	0	0	0	1	1	0	0	0	0	1	1	1	1	0	0
ISKpn34	1	0	1	1	0	0	1	1	0	1	0	0	0	0	1	1	0	0
ISKpn37	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISKpn38	0	1	1	1	0	0	1	1	1	1	0	1	1	1	1	1	0	0
ISKpn4	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISKpn40	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISKpn41	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISKpn42	0	0	1	0	0	0	0	1	1	1	0	0	0	0	1	1	0	0
ISKpn43	0	1	1	1	0	0	0	1	0	1	0	0	0	1	1	1	0	0
ISKpn47	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0
ISKpn49	1	0	1	1	0	0	1	1	1	0	0	0	0	1	1	1	0	0
ISKpn50	0	0	1	0	0	0	1	1	1	0	0	0	0	0	1	1	0	0
ISKpn53	1	0	1	0	1	0	1	0	1	1	1	1	0	0	1	1	0	0
ISKpn54	1	1	1	0	0	0	1	0	1	0	1	1	1	0	1	1	0	0
ISKpn6	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0	0
ISKpn60	1	1	1	1	0	0	1	1	1	1	0	1	1	1	1	1	0	0
ISKpn66	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISKpn7	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISKpn8	1	1	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
ISKqu3	0	1	0	0	0	0	0	1	1	1	0	1	0	1	1	1	0	0
ISLad1	1	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
ISLad2	1	1	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
ISLad6	0	0	1	1	0	0	1	1	1	1	0	0	1	1	0	1	0	0
ISLead2	0	0	1	1	0	0	0	0	0	1	0	0	0	0	1	1	0	0
ISLead3	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISLpn8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISMaq2	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0
ISMasp6	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISMph1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
ISMtsp21	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0
ISNisp5	0	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0
ISNpu13	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPa16	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISPa21	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPa31	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISPa38	0	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0	0
ISPa7	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISPa77	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPa80	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ISPa83	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPa85	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPa95	0	0	0	0	0	0	1	1	1	1	0	0	0	0	0	1	0	0
ISPa97	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISPa99	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
ISPan1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0
ISPcc1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISPcc2	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPcc4	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPcc6	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0
ISPeat2	1	0	0	1	0	0	1	1	0	0	0	0	0	0	0	1	0	0
ISPlge2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISPlge3	1	1	0	1	0	0	0	1	0	0	0	0	0	0	1	1	0	0
ISPlge4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPlu15	1	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0
ISPlu22	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
ISPlu8	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPmi3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPpr10	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPps1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISPpu1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISPpu12	0	0	1	1	0	0	1	0	1	1	0	0	0	0	0	1	0	0
ISPpu22	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPpu23	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPpu30	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPre2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPrre1	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0
ISPrre10	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	0
ISPrst3	1	0	1	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISPst2	0	0	1	1	0	0	0	1	1	1	0	0	0	0	1	1	0	0
ISPst3	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISPst4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPst8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPst9	0	0	1	1	0	0	1	0	1	0	0	0	0	0	0	1	0	0
ISPsy30	0	0	0	0	0	0	1	1	1	1	0	0	1	0	0	1	0	0
ISPsy42	0	1	0	1	0	0	0	1	0	1	0	0	0	0	1	1	0	0
ISPsy43	0	0	0	1	0	0	1	1	0	1	0	1	0	1	1	1	0	0
ISPye16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
ISRaq1	1	1	1	1	1	1	1	1	0	1	0	0	1	0	1	1	0	0
ISRhba1	0	0	1	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0
ISRop1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1	1	0	0
ISRor2	1	0	1	1	1	0	0	1	1	0	1	0	1	1	1	1	1	0
ISRor3	1	0	1	1	1	0	0	1	1	0	1	1	1	1	1	1	0	0
ISRosp3	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0
ISRso16	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0
ISRso21	1	0	1	0	1	0	1	0	1	0	1	0	0	0	1	0	0	0
ISSaen1	1	0	1	0	0	0	1	0	1	0	0	0	1	0	1	1	0	0
ISSav7	1	0	1	0	1	0	1	0	1	0	1	0	1	0	1	0	1	0
ISSba1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSba14	0	1	1	0	0	0	1	1	1	1	0	1	0	0	0	1	0	0
ISSba3	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSba8	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISSbo1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISSd1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISSde4	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0
ISSde5	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISSen1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISSen10	0	0	1	0	0	0	1	0	0	1	0	0	1	1	1	0	0	0
ISSen13	0	0	1	0	0	0	0	1	1	1	0	0	1	0	1	1	0	0
ISSen3	0	0	1	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
ISSen4	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	1
ISSen6	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISSen7	0	0	1	1	0	0	0	1	1	0	0	0	1	1	1	1	0	0
ISSen8	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0
ISSen9	0	0	0	1	0	0	1	1	0	1	0	0	0	1	1	1	0	0
ISSfl1	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0
ISSfl10	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISSfl3	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISSfl8	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISSfl9	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0
ISSgsp1	0	0	1	1	0	0	1	1	1	1	0	1	1	0	1	1	0	0
ISShdy1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISShdy2	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISShes11	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0
ISShes4	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ISShfr8	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISShfr9	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0
ISShwo2	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ISSlo2	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISSm1	0	0	1	1	0	0	1	1	0	1	0	0	0	1	0	1	0	0
ISSm3	0	0	1	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0
ISSm4	1	0	1	1	1	0	1	1	1	1	1	0	0	0	1	1	1	0
ISSod16	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSod23	0	0	1	0	0	0	0	0	1	1	0	0	1	0	1	0	0	0
ISSod4	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
ISSod6	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSoEn2	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0
ISSoEn3	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISSpe2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISSpr1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISSpr2	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISSpu11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISSpu2	0	0	0	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
ISSpu20	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISSpu6	1	1	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0
ISSpu7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISSso4	1	1	0	0	0	0	0	1	1	1	0	0	0	0	0	1	0	0
ISSsu9	1	1	1	1	1	0	1	1	1	1	1	1	1	0	1	1	1	0
ISStma11	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	0
ISSty2	0	0	0	0	0	0	1	0	0	0	0	1	1	0	1	1	0	0
ISSysp7	1	1	1	0	0	0	1	1	1	0	1	0	1	0	0	1	0	0
ISTfu1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISThi1	0	0	0	0	0	0	1	0	1	1	0	0	0	0	1	1	0	0
ISThsp9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISUnCu16	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISVch3	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISVch4	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISVch9	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISVsa17	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
ISVsa3	0	0	0	1	0	0	0	1	1	1	0	0	0	0	1	1	0	0
ISWpi13	0	0	1	1	0	0	1	1	1	1	0	0	0	1	0	1	0	0
ISXc4	0	0	0	1	0	0	0	1	0	1	0	0	0	0	1	1	0	0
ISXne1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISYal1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISYen3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
ISYpe1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISYps1	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISYps3	0	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	0	0
ISYps8	1	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISYru1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0	0
Tn2	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
Tn3	0	1	0	1	0	0	0	1	1	1	0	0	0	0	1	1	0	0
Tn4430	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Tn5393	0	1	0	1	0	1	1	1	1	1	0	0	0	1	1	1	0	0
Tn5403	0	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	1
TnAs1	1	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0	0
TnAs2	0	1	1	1	0	0	1	1	1	1	0	1	1	0	1	1	0	0
TnAs3	1	1	1	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
TnEc1	1	0	0	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
TnShfr1	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	1	0	0
TnXax1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

Insertion Sequences UpsetR

#----------------Step3: UpsetR Plot VFGenes

IS_upsetR_df <- Prokka_IS_plasClass_df  %>% select(IS,Species_ContigClass)
#View(IS_upsetR_df)

IS_upsetR_lt <- split(IS_upsetR_df$IS, IS_upsetR_df$Species_ContigClass) 
IS_comb_mat = make_comb_mat(IS_upsetR_lt)

col_size = comb_size(IS_comb_mat)
row_size = set_size(IS_comb_mat)

IS_ht = UpSet(IS_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "Insertion Sequences Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
           # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" ,
           #            "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(IS_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(IS_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(900, units = "pt"), height = unit(12, "cm"))

IS_ht = draw(IS_ht)
#ht
col_od = column_order(IS_ht)
row_od = row_order(IS_ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})